【作者】 宏哲；

【导师】 尹连庆；

【作者基本信息】 华北电力大学（河北） ， 环境科学， 2007， 硕士

【摘要】 本文采用BP人工神经网络技术,建立火电厂水力输灰系统(输灰管道和回水管道)结垢预测模型,旨在实现对水力输灰系统结垢预测的研究。研究了水力输灰系统结垢的动力学机理,从本质上分析了结垢的原因。通过现场调研和查阅资料,分析总结了水力输灰系统结垢速率的影响因素。进行了正交实验和单因素实验,确定了影响输灰管道结垢速率的主要因素及其影响规律。水力输灰系统结垢趋势和结垢程度是众多因素综合作用的结果,各种因素要综合考虑。因此,本文利用BP人工神经网络在MATLAB 6.5上的实现,分别建立了满足误差要求的输灰管道和回水管道结垢预测模型,实现了对水力输灰系统结垢的预测。更多还原

【Abstract】 This paper aims at studying scaling prediction of the wet ash transportation system by building up the scaling prediction model of the pipeline of transport ash and backwater in coal-fired power plant. It studies the kinetics mechanism of scaling in the wet ash transportation system and analyzes the reasons for scaling in essence. It summarizes the influencing factors of the scaling rate in the wet ash transportation system by field investigation and data access. After that, the four main factors of transport ash pipeline and their effect law are identified by orthogonal experiment and single-factor experiment. But, the production of scale is a result of numerous factors, and it has to solve the problem by summary analysis. Therefore, at last, the scaling prediction model in the pipeline of transport ash and backwater is built up respectively by Realization of BP Networks on MATLAB6.5 .It completes the scaling prediction in the wet ash transportation system.更多还原